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For large-scale visual search, highly compressed yet meaningful representations of images are essential. Structured vector quantizers based on product quantization and its variants are usually employed to achieve such compression while minimizing the loss of accuracy. Yet, unlike binary hashing schemes, these unsupervised methods have not yet benefited from the supervision, end-to-end learning and...
In this work we propose novel joint and sequential multimodal approaches for the task of single channel audio source separation in videos. This is done within the popular non-negative matrix factorization framework using information about the sounding object's motion. Specifically, we present methods that utilize non-negative least squares formulation to couple motion and audio information. The proposed...
At the core of vision research is the notion of perceptual invariance. The question of how the visual system is able to develop stable or invariant states through the ever transforming environment is central to understanding the brain's recognition process. The coined term slowness principle used in slow feature analysis is a reference to the brain's ability to generate slow changing and thus stable...
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